Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.)

Authors

• Mario Soto, University of Arkansas, Fayetteville
• Aurelie M. Poncet, University of Arkansas, Fayetteville
• Nilda Roma-Burgos, University of Arkansas, Fayetteville
• O. Wesley France, University of Arkansas, Fayetteville
• Juan C. Velasquez, University of Arkansas, Fayetteville
• Amanda J. Ashworth, USDA
• Kristofor R. Brye, University of Arkansas, Fayetteville
• Cengiz Koparan, University of Arkansas, Fayetteville

Document Type

Article

Publication Date

8-2025

Keywords

Dimensionality reduction, Glyphosate efficacy, Normalization, Precision agriculture, Proximal sensing, Sensor, Weeds

Abstract

Hyperspectral sensors are increasingly used to develop optimized vegetation indices (VIs) that capture plant spectral response to specific stressors. The project goal was to develop quantitative metrics for characterization of weed response to herbicide application. This work applied hyperspectral sensing to describe and predict the spectral response of common lambsquarters (Chenopodium album L., CHEAL) to glyphosate application. Thirteen treatments, including one glyphosate rate used alone or in combination with eleven adjuvants plus one non- treated control, were applied to CHEAL seedlings cultivated in a greenhouse. Visible injury ratings and non- imaging hyperspectral data were collected 14 days after treatment application. Sensor data processing included cleaning, normalization, smoothing, and spectral reduction. The treatments resulted in a significant (P < 0.001) gradient of injury ranging from 0 to 98 %, with visible differences in leaf spectral signatures. Thirty-one key wavelengths were identified using principal component analysis, relief-f feature selection, and Bayesian discriminant analysis and used to create 45,732 VIs. No single VI accurately described CHEAL injury (minimum mean absolute error (MAE) = 14.0 %). A random forest algorithm developed using four VIs adequately described CHEAL injury with an MAE of 7.7 %. Post-calibration was not needed to improve the random forest model performance (P >= 0.05). Therefore, hyperspectral sensing could be used to quantify weed response to herbicide application and overcome the limitations of visual methods current in use. Further development of this method and validation will allow development of a platform for high-throughput phenotyping of weed response to herbicide application and screening for herbicide resistance.

Comments

Web of Science

Elsevier

Citation

Mario Soto, Aurelie M. Poncet, Nilda Roma-Burgos, O. Wesley France, Juan C. Velasquez, Amanda J. Ashworth, Kristofor R. Brye, Cengiz Koparan, Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.), Smart Agricultural Technology, Volume 11, 2025, 100890, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2025.100890.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.